




'''simple docstring'''







from __future__ import annotations

import math
import random
from typing import Any

class 	_snake_case:






 def __init__(self       :  List[Any]    )       ->   None:






    """simple docstring"""


    A__       =     []
    A__       =     0
    A__       =     0






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   bool:






    """simple docstring"""


    return self.head == self.tail






 def 						_UpperCamelCase (self       :  Tuple    ,		a       :  Any    )       ->   None:






    """simple docstring"""


    self.data.append(a    )
    A__       =     self.tail + 1






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Any:






    """simple docstring"""


    A__       =     self.data[self.head]
    A__       =     self.head + 1
    return ret






 def 						_UpperCamelCase (self       :  str    )       ->   int:






    """simple docstring"""


    return self.tail - self.head






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   None:






    """simple docstring"""


    print(self.data    )
    print('**************'    )
    print(self.data[self.head : self.tail]    )




class 	_snake_case:






 def __init__(self       :  Tuple    ,		a       :  Any    )       ->   None:






    """simple docstring"""


    A__       =     data
    A__       =     None
    A__       =     None
    A__       =     1






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Any:






    """simple docstring"""


    return self.data






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   MyNode | None:






    """simple docstring"""


    return self.left






 def 						_UpperCamelCase (self       :  Any    )       ->   MyNode | None:






    """simple docstring"""


    return self.right






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   int:






    """simple docstring"""


    return self.height






 def 						_UpperCamelCase (self       :  str    ,		a       :  Any    )       ->   None:






    """simple docstring"""


    A__       =     data






 def 						_UpperCamelCase (self       :  int    ,		a       :  MyNode | None    )       ->   None:






    """simple docstring"""


    A__       =     node






 def 						_UpperCamelCase (self       :  int    ,		a       :  MyNode | None    )       ->   None:






    """simple docstring"""


    A__       =     node






 def 						_UpperCamelCase (self       :  int    ,		a       :  int    )       ->   None:






    """simple docstring"""


    A__       =     height







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if node is None:
      return 0
   return node.get_height()







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   if a > b:
      return a
   return b







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   print('left rotation node:'      ,node.get_data()						)
   A__       =     node.get_left()
   assert ret is not None
   node.set_left(ret.get_right()						)
   ret.set_right(UpperCAmelCase						)
   A__       =     my_max(get_height(node.get_right()						)      ,get_height(node.get_left()						)						) + 1
   node.set_height(UpperCAmelCase						)
   A__       =     my_max(get_height(ret.get_right()						)      ,get_height(ret.get_left()						)						) + 1
   ret.set_height(UpperCAmelCase						)
   return ret







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   print('right rotation node:'      ,node.get_data()						)
   A__       =     node.get_right()
   assert ret is not None
   node.set_right(ret.get_left()						)
   ret.set_left(UpperCAmelCase						)
   A__       =     my_max(get_height(node.get_right()						)      ,get_height(node.get_left()						)						) + 1
   node.set_height(UpperCAmelCase						)
   A__       =     my_max(get_height(ret.get_right()						)      ,get_height(ret.get_left()						)						) + 1
   ret.set_height(UpperCAmelCase						)
   return ret







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     node.get_left()
   assert left_child is not None
   node.set_left(left_rotation(UpperCAmelCase						)						)
   return right_rotation(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     node.get_right()
   assert right_child is not None
   node.set_right(right_rotation(UpperCAmelCase						)						)
   return left_rotation(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   if node is None:
      return MyNode(UpperCAmelCase						)
   if data < node.get_data():
      node.set_left(insert_node(node.get_left()      ,UpperCAmelCase						)						)
      if (
          get_height(node.get_left()						) - get_height(node.get_right()						) == 2
      ):  # an unbalance detected
         A__       =     node.get_left()
         assert left_child is not None
         if (
             data < left_child.get_data()
         ):  # new node is the left child of the left child
            A__       =     right_rotation(UpperCAmelCase						)
         else:
            A__       =     lr_rotation(UpperCAmelCase						)
   else:
      node.set_right(insert_node(node.get_right()      ,UpperCAmelCase						)						)
      if get_height(node.get_right()						) - get_height(node.get_left()						) == 2:
         A__       =     node.get_right()
         assert right_child is not None
         if data < right_child.get_data():
            A__       =     rl_rotation(UpperCAmelCase						)
         else:
            A__       =     left_rotation(UpperCAmelCase						)
   A__       =     my_max(get_height(node.get_right()						)      ,get_height(node.get_left()						)						) + 1
   node.set_height(UpperCAmelCase						)
   return node







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   while True:
      A__       =     root.get_right()
      if right_child is None:
         break
      A__       =     right_child
   return root.get_data()







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   while True:
      A__       =     root.get_left()
      if left_child is None:
         break
      A__       =     left_child
   return root.get_data()







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     root.get_left()
   A__       =     root.get_right()
   if root.get_data() == data:
      if left_child is not None and right_child is not None:
         A__       =     get_left_most(UpperCAmelCase						)
         root.set_data(UpperCAmelCase						)
         root.set_right(del_node(UpperCAmelCase      ,UpperCAmelCase						)						)
      elif left_child is not None:
         A__       =     left_child
      elif right_child is not None:
         A__       =     right_child
      else:
         return None
   elif root.get_data() > data:
      if left_child is None:
         print('No such data'						)
         return root
      else:
         root.set_left(del_node(UpperCAmelCase      ,UpperCAmelCase						)						)
   else:  # root.get_data() < data
      if right_child is None:
         return root
      else:
         root.set_right(del_node(UpperCAmelCase      ,UpperCAmelCase						)						)

   if get_height(UpperCAmelCase						) - get_height(UpperCAmelCase						) == 2:
      assert right_child is not None
      if get_height(right_child.get_right()						) > get_height(right_child.get_left()						):
         A__       =     left_rotation(UpperCAmelCase						)
      else:
         A__       =     rl_rotation(UpperCAmelCase						)
   elif get_height(UpperCAmelCase						) - get_height(UpperCAmelCase						) == -2:
      assert left_child is not None
      if get_height(left_child.get_left()						) > get_height(left_child.get_right()						):
         A__       =     right_rotation(UpperCAmelCase						)
      else:
         A__       =     lr_rotation(UpperCAmelCase						)
   A__       =     my_max(get_height(root.get_right()						)      ,get_height(root.get_left()						)						) + 1
   root.set_height(UpperCAmelCase						)
   return root

class 	_snake_case:






 def __init__(self       :  Any    )       ->   None:






    """simple docstring"""


    A__       =     None






 def 						_UpperCamelCase (self       :  Any    )       ->   int:






    """simple docstring"""


    return get_height(self.root    )






 def 						_UpperCamelCase (self       :  Any    ,		a       :  Any    )       ->   None:






    """simple docstring"""


    print('insert:' + str(a    )    )
    A__       =     insert_node(self.root    ,		a    )






 def 						_UpperCamelCase (self       :  str    ,		a       :  Any    )       ->   None:






    """simple docstring"""


    print('delete:' + str(a    )    )
    if self.root is None:
       print('Tree is empty!'    )
       return
    A__       =     del_node(self.root    ,		a    )






 def __str__(self       :  Any    ,		)       ->   str:  # a level traversale, gives a more intuitive look on the tree






    """simple docstring"""


    A__       =     ''
    A__       =     MyQueue()
    q.push(self.root    )
    A__       =     self.get_height()
    if layer == 0:
       return output
    A__       =     0
    while not q.is_empty():
       A__       =     q.pop()
       A__       =     ' ' * int(math.pow(2    ,		layer - 1    )    )
       output += space
       if node is None:
          output += "*"
          q.push(a    )
          q.push(a    )
       else:
          output += str(node.get_data()    )
          q.push(node.get_left()    )
          q.push(node.get_right()    )
       output += space
       A__       =     cnt + 1
       for i in range(1_00    ):
          if cnt == math.pow(2    ,		a    ) - 1:
             A__       =     layer - 1
             if layer == 0:
                output += "\n*************************************"
                return output
             output += "\n"
             break
    output += "\n*************************************"
    return output







def 						_A      ( ):






   '''simple docstring'''







   import doctest

   doctest.testmod()


if __name__ == "__main__":
    _test()
    lowerCAmelCase_     						=				AVLtree()
    lowerCAmelCase_     						=				list(range(1_0))
    random.shuffle(lst)
    for i in lst:
        t.insert(i)
        print(str(t))
    random.shuffle(lst)
    for i in lst:
        t.del_node(i)
        print(str(t))








'''simple docstring'''







import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase_     						=				'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_     						=				direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase_     						=				re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase_     						=				re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase_     						=				re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase_     						=				[
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'      ,UpperCAmelCase						)
   return [m.group(0						) for m in matches]







def 						_A      ( ):






   '''simple docstring'''







   A__       =     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   A__       =     {
       config.replace('Config'      ,''						): model_type for model_type, config in config_maping_names.items()
   }

   # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)

   # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
   for attr_name in dir(UpperCAmelCase						):
      A__       =     None
      if _re_tf_models.match(UpperCAmelCase						) is not None:
         A__       =     tf_models
         A__       =     _re_tf_models.match(UpperCAmelCase						).groups()[0]
      elif _re_flax_models.match(UpperCAmelCase						) is not None:
         A__       =     flax_models
         A__       =     _re_flax_models.match(UpperCAmelCase						).groups()[0]
      elif _re_pt_models.match(UpperCAmelCase						) is not None:
         A__       =     pt_models
         A__       =     _re_pt_models.match(UpperCAmelCase						).groups()[0]

      if lookup_dict is not None:
         while len(UpperCAmelCase						) > 0:
            if attr_name in model_prefix_to_model_type:
               A__       =     True
               break
            # Try again after removing the last word in the name
            A__       =     ''.join(camel_case_split(UpperCAmelCase						)[:-1]						)

   A__       =     set(list(pt_models.keys()						) + list(tf_models.keys()						) + list(flax_models.keys()						)						)
   A__       =     list(UpperCAmelCase						)
   all_models.sort()

   A__       =     {'model_type': all_models}
   A__       =     [pt_models[t] for t in all_models]
   A__       =     [tf_models[t] for t in all_models]
   A__       =     [flax_models[t] for t in all_models]

   # Now let's use the auto-mapping names to make sure
   A__       =     {}
   for t in all_models:
      if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
         A__       =     'AutoProcessor'
      elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
         A__       =     'AutoTokenizer'
      elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
         A__       =     'AutoFeatureExtractor'
      else:
         # Default to AutoTokenizer if a model has nothing, for backward compatibility.
         A__       =     'AutoTokenizer'

   A__       =     [processors[t] for t in all_models]

   return pd.DataFrame(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     [
       transformers_module.models.auto.modeling_auto,
       transformers_module.models.auto.modeling_tf_auto,
       transformers_module.models.auto.modeling_flax_auto,
   ]
   for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
      A__       =     [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
      A__       =     [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
      # Loop through all three frameworks
      for module, cls, mapping in zip(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         # The type of pipeline may not exist in this framework
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						):
            continue
         # First extract all model_names
         A__       =     []
         for name in getattr(UpperCAmelCase      ,UpperCAmelCase						).values():
            if isinstance(UpperCAmelCase      ,UpperCAmelCase						):
               model_names.append(UpperCAmelCase						)
            else:
               model_names.extend(list(UpperCAmelCase						)						)

            # Add pipeline tag and auto model class for those models
         table.update({model_name: (pipeline_tag, cls) for model_name in model_names}						)

   return table







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_frameworks_table()
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   A__       =     hf_hub_download(
       'huggingface/transformers-metadata'      ,'pipeline_tags.json'      ,repo_type='dataset'      ,token=UpperCAmelCase						)
   A__       =     Dataset.from_json(UpperCAmelCase						)
   A__       =     {
       tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
       for i in range(len(UpperCAmelCase						)						)
   }
   A__       =     update_pipeline_and_auto_class_table(UpperCAmelCase						)

   # Sort the model classes to avoid some nondeterministic updates to create false update commits.
   A__       =     sorted(table.keys()						)
   A__       =     pd.DataFrame(
       {
           'model_class': model_classes,
           'pipeline_tag': [table[m][0] for m in model_classes],
           'auto_class': [table[m][1] for m in model_classes],
       }						)
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   with tempfile.TemporaryDirectory() as tmp_dir:
      frameworks_dataset.to_json(os.path.join(UpperCAmelCase      ,'frameworks.json'						)						)
      tags_dataset.to_json(os.path.join(UpperCAmelCase      ,'pipeline_tags.json'						)						)

      if commit_sha is not None:
         A__       =     (
             F"""Update with commit {commit_sha}\n\nSee: """
             F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
         )
      else:
         A__       =     'Update'

      upload_folder(
          repo_id='huggingface/transformers-metadata'      ,folder_path=UpperCAmelCase      ,repo_type='dataset'      ,token=UpperCAmelCase      ,commit_message=UpperCAmelCase      ,)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
   A__       =     transformers_module.pipelines.SUPPORTED_TASKS
   A__       =     []
   for key in pipeline_tasks:
      if key not in in_table:
         A__       =     pipeline_tasks[key]['pt']
         if isinstance(UpperCAmelCase      ,(list, tuple)						):
            A__       =     model[0]
         A__       =     model.__name__
         if model not in in_table.values():
            missing.append(UpperCAmelCase						)

   if len(UpperCAmelCase						) > 0:
      A__       =     ', '.join(UpperCAmelCase						)
      raise ValueError(
          'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
          F"""`utils/update_metadata.py`: {msg}. Please add them!"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
    parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
    parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
    lowerCAmelCase_     						=				parser.parse_args()

    if args.check_only:
        check_pipeline_tags()
    else:
        update_metadata(args.token, args.commit_sha)


